



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 			__magic_name__ (				snake_case__    ):
		lowercase   :							int        =(IPNDMScheduler,)
		lowercase   :							List[str]        =(('''num_inference_steps''', 50),)






		def SCREAMING_SNAKE_CASE_      (    self   :  Tuple					,		**UpperCamelCase__   :  Any   )					->       str:
								'''simple docstring'''







								UpperCAmelCase								=  {"num_train_timesteps": 10_00}
								config.update(**__snake_case   )
								return config






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]					,		UpperCamelCase__   :  Optional[Any]=0					,		**UpperCamelCase__   :  str   )					->       str:
								'''simple docstring'''







								UpperCAmelCase								=  dict(self.forward_default_kwargs   )
								UpperCAmelCase								=  kwargs.pop("num_inference_steps"					,		__snake_case   )
								UpperCAmelCase								=  self.dummy_sample
								UpperCAmelCase								=  0.1 * sample
								UpperCAmelCase								=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
														UpperCAmelCase								=  self.get_scheduler_config(**__snake_case   )
														UpperCAmelCase								=  scheduler_class(**__snake_case   )
														scheduler.set_timesteps(__snake_case   )
														# copy over dummy past residuals
														UpperCAmelCase								=  dummy_past_residuals[:]

														if time_step is None:
																				UpperCAmelCase								=  scheduler.timesteps[len(scheduler.timesteps   ) // 2]

														with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(__snake_case   )
																				UpperCAmelCase								=  scheduler_class.from_pretrained(__snake_case   )
																				new_scheduler.set_timesteps(__snake_case   )
																				# copy over dummy past residuals
																				UpperCAmelCase								=  dummy_past_residuals[:]

														UpperCAmelCase								=  scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample
														UpperCAmelCase								=  new_scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample

														assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

														UpperCAmelCase								=  scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample
														UpperCAmelCase								=  new_scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample

														assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[int]   )					->       Tuple:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]					,		UpperCamelCase__   :  str=0					,		**UpperCamelCase__   :  Union[str, Any]   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  dict(self.forward_default_kwargs   )
								UpperCAmelCase								=  kwargs.pop("num_inference_steps"					,		__snake_case   )
								UpperCAmelCase								=  self.dummy_sample
								UpperCAmelCase								=  0.1 * sample
								UpperCAmelCase								=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
														UpperCAmelCase								=  self.get_scheduler_config()
														UpperCAmelCase								=  scheduler_class(**__snake_case   )
														scheduler.set_timesteps(__snake_case   )

														# copy over dummy past residuals (must be after setting timesteps)
														UpperCAmelCase								=  dummy_past_residuals[:]

														if time_step is None:
																				UpperCAmelCase								=  scheduler.timesteps[len(scheduler.timesteps   ) // 2]

														with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(__snake_case   )
																				UpperCAmelCase								=  scheduler_class.from_pretrained(__snake_case   )
																				# copy over dummy past residuals
																				new_scheduler.set_timesteps(__snake_case   )

																				# copy over dummy past residual (must be after setting timesteps)
																				UpperCAmelCase								=  dummy_past_residuals[:]

														UpperCAmelCase								=  scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample
														UpperCAmelCase								=  new_scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample

														assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

														UpperCAmelCase								=  scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample
														UpperCAmelCase								=  new_scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample

														assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"






		def SCREAMING_SNAKE_CASE_      (    self   :  Dict					,		**UpperCamelCase__   :  Tuple   )					->       Tuple:
								'''simple docstring'''







								UpperCAmelCase								=  self.scheduler_classes[0]
								UpperCAmelCase								=  self.get_scheduler_config(**__snake_case   )
								UpperCAmelCase								=  scheduler_class(**__snake_case   )

								UpperCAmelCase								=  10
								UpperCAmelCase								=  self.dummy_model()
								UpperCAmelCase								=  self.dummy_sample_deter
								scheduler.set_timesteps(__snake_case   )

								for i, t in enumerate(scheduler.timesteps   ):
														UpperCAmelCase								=  model(__snake_case					,		__snake_case   )
														UpperCAmelCase								=  scheduler.step(__snake_case					,		__snake_case					,		__snake_case   ).prev_sample

								for i, t in enumerate(scheduler.timesteps   ):
														UpperCAmelCase								=  model(__snake_case					,		__snake_case   )
														UpperCAmelCase								=  scheduler.step(__snake_case					,		__snake_case					,		__snake_case   ).prev_sample

								return sample






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]   )					->       Dict:
								'''simple docstring'''







								UpperCAmelCase								=  dict(self.forward_default_kwargs   )

								UpperCAmelCase								=  kwargs.pop("num_inference_steps"					,		__snake_case   )

								for scheduler_class in self.scheduler_classes:
														UpperCAmelCase								=  self.get_scheduler_config()
														UpperCAmelCase								=  scheduler_class(**__snake_case   )

														UpperCAmelCase								=  self.dummy_sample
														UpperCAmelCase								=  0.1 * sample

														if num_inference_steps is not None and hasattr(__snake_case					,		"set_timesteps"   ):
																				scheduler.set_timesteps(__snake_case   )
														elif num_inference_steps is not None and not hasattr(__snake_case					,		"set_timesteps"   ):
																				UpperCAmelCase								=  num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														UpperCAmelCase								=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														UpperCAmelCase								=  dummy_past_residuals[:]

														UpperCAmelCase								=  scheduler.timesteps[5]
														UpperCAmelCase								=  scheduler.timesteps[6]

														UpperCAmelCase								=  scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample
														UpperCAmelCase								=  scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample

														self.assertEqual(output_a.shape					,		sample.shape   )
														self.assertEqual(output_a.shape					,		output_a.shape   )

														UpperCAmelCase								=  scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample
														UpperCAmelCase								=  scheduler.step(__snake_case					,		__snake_case					,		__snake_case					,		**__snake_case   ).prev_sample

														self.assertEqual(output_a.shape					,		sample.shape   )
														self.assertEqual(output_a.shape					,		output_a.shape   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[int]   )					->       Optional[Any]:
								'''simple docstring'''







								for timesteps in [1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case					,		time_step=__snake_case   )






		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Tuple:
								'''simple docstring'''







								for t, num_inference_steps in zip([1, 5, 10]					,		[10, 50, 1_00]   ):
														self.check_over_forward(num_inference_steps=__snake_case					,		time_step=__snake_case   )







		def SCREAMING_SNAKE_CASE_      (    self   :  Any   )					->       Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  self.full_loop()
								UpperCAmelCase								=  torch.mean(torch.abs(__snake_case   )   )

								assert abs(result_mean.item() - 2_54_05_29   ) < 10







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					int      =       {
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'deit'
    def __init__(			self     ,   __snake_case=7_6_8     ,   __snake_case=1_2     ,   __snake_case=1_2     ,   __snake_case=3_0_7_2     ,   __snake_case="gelu"     ,   __snake_case=0.0     ,   __snake_case=0.0     ,   __snake_case=0.02     ,   __snake_case=1E-12     ,   __snake_case=2_2_4     ,   __snake_case=1_6     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=1_6     ,   **__snake_case     ,   ):
         super().__init__(**__snake_case       )

         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							initializer_range
         snake_case											=							layer_norm_eps
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							qkv_bias
         snake_case											=							encoder_stride






class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						version.parse('1.11'      )
    @property
    def 						a_     (			self       ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )




    @property
    def 						a_     (			self       ):
         return 1E-4




